Apparatus and method for computing maximum power reduction for a umts signal

ABSTRACT

A method and apparatus are provided for controlling transmit power with an estimated value of cubic metric (CM) and/or peak-to-average ratio (PAR). Preferably, the method is applied in determining a value for Maximum Power Reduction (MPR) for computing maximum-MPR or minimum-MPR, by estimating CM and/or PAR from signal parameters. The method of estimating CM and/or PAR is applicable to any multicode signal.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/106,246 filed Apr. 18, 2008, which claims the benefit of U.S.Provisional Application No. 60/912,947 filed Apr. 20, 2007 and U.S.Provisional Application No. 61/027,281 filed Feb. 8, 2008, which areincorporated by reference as if fully set forth.

FIELD OF INVENTION

This application is related to wireless communications.

BACKGROUND

In practical amplifier circuits, such as those in a Universal MobileTelecommunications System (UMTS) Wireless Transmit Receive Unit (WTRU)transmit chain, the cause of spectral re-growth is due to nonlinearamplifier characteristics. The term spectral re-growth describes theincrease in out-of-band signal energy at the power amplifier output.Spectral re-growth due to non-linear amplifier effects is mostpronounced within a channel adjacent to a desired transmit channel. ForUMTS, the requirement on the power amplifier is defined by an AdjacentChannel Leakage Ratio (ACLR) at +/−5 MHz of the desired channel. Theamplifier voltage gain characteristic is as follow:

v _(o)(t)=g ₁ ·v ₁(t)+g ₂ ·v _(i)(t)² +g ₃ ·v _(i)(t)³ + . . . +g _(n)·v _(i)(t)^(n)  (On Equation (1)

where g₁·v_(i)(t) is the linear gain of the amplifier and the remainingterms (i.e., g₂·v_(i)(t)²+g₃·v_(i)(t)³+ . . . +g_(n)·v_(i)(t)^(n))represent the non-linear gain. If the signal carries a modulated thirdGeneration Partnership Project (3GPP) Radio Frequency (RF), then thenon-linear terms will be generated as a result of inter-modulationdistortion, resulting in in-band distortion terms causing an increase inError Vector Magnitude (EVM) and out-of-band distortion causing anincrease in ACLR. Both effects cause a reduction of modulation quality.

Multi-code signals, such as those in UMTS Release 5 and Release 6,exhibit an increase in peak-to-average power resulting in larger dynamicsignal variations. These increased signal variations require increasedamplifier linearity, resulting in increased power consumption. Recentresults have shown that it is not efficient to directly transfer dB fordB (i.e., the ratio of peak power to average power of a signal, alsoknown as peak-to-average ratio (PAR)) to amplifier power reduction.Analysis of the amplifier spectral re-growth has shown that the 3^(rd)order non-linear gain term (“cubic gain”) is the dominant cause of ACLRincrease. The total energy in the cubic term is dependent on thestatistical distribution of the input signal.

With the introduction of High Speed Uplink Packet Access (HSUPA), a newmethod of estimating amplifier power reduction called the Cubic Metric(CM) was introduced in Release 6. The CM is based on the amplifier cubicgain term. The CM describes the ratio of the cubic components in theobserved signal to the cubic components of a 12.2 kbps voice referencesignal. The CM applies to both High Speed Downlink Packet Access (HSDPA)and HSUPA uplink signals. Statistical analysis has shown that the powerde-rating based on an estimation of the CM exhibits a significantlysmaller error distribution when compared to power de-rating based on99.9% PAR, where the error distribution is the difference between theactual power de-rating and the estimated power de-rating.

3GPP specifies a Maximum Power Reduction (MPR) test which verifies thatthe maximum transmit power of a WTRU is greater than or equal to thenominal maximum transmit power less an amount herein termed“maximum-MPR,” where maximum-MPR is a function of the transmittedsignal's CM. For a given power amplifier, the manufacturer coulddetermine that its device could allow that maximum power need only belimited to some amount, herein termed “minimum-MPR,” that is less thanmaximum-MPR, and yet remain 3GPP ACLR compliant. While “minimum-MPR”could be specified as a function of CM, it is possible that it couldalternatively be specified as a function of a particular percentage PAR.Limiting maximum power by minimum-MPR rather than maximum-MPR allows theWTRU to transmit at a higher maximum power and thus provides a WTRUmanufacturer exploiting minimum-MPR with a competitive advantage. It ispossible that a particular WTRU's design could include determining bothmaximum-MPR and minimum-MPR and making a selection between the two.

Regardless of the choice of using maximum-MPR or minimum-MPR, a keyissue is that the WTRU must know the value of CM and/or PAR in order tocompute the selected MPRs and, if required, (i.e., if the WTRU isoperating at near maximum power), ultimately use them to actually setthe transmit power. Any multicode signal (characterized by the physicalchannels being transmitted, their channelization codes and weightscalled β terms) has its particular CM and PAR.

In UMTS, the signal, and thus the CM and PAR, can change every 2 or 10msec Transmit Time Interval (TTI). It can be shown that for Release 6UMTS there are over two hundred thousand combinations of physicalchannel parameters and quantized β terms; each such combination isherein termed a possible signal. The large number of possible signalsmakes a strict one-to-one a priori determined lookup of CM or PAR as afunction of signal characteristics impractical to implement in realtime; particularly in a small lower power handheld device operating atUMTS data rates. Recognizing that it is impractical for a WTRU to simplylook up CM or PAR, it is necessary to either measure them or estimatethem within some allowable error, from the signal's characteristicparameters.

Measuring CM or PAR from the actual signal is well known. A key defectin this is that the signal must first be created in order to make themeasurement. Since transmit power may ultimately be set as a function ofCM and/or PAR, setting the power by measurement will require a signal,or at least some time limited segment of it, to be generated before itis transmitted. While in theory this is possible, the time latencyrequirement of UMTS and practical memory limitations makes this approachalso impractical.

A variant of the above approach is to generate and start transmittingthe signal at some transmit power level computed from a “guess” of CM orPAR and then adjust the transmit power for the remaining whole timeslotsin a TTI to a second power level. The mix of first and second powerlevels is computed such that, on average, the power level is close tothe level that would have been selected had CM or PAR been known priorto the start of the TTI.

In UMTS there are 15 timeslots in a 10 msec TTI, but only threetimeslots in a 2 msec TTI. Assuming, for example, that the measurementof CM or PAR takes some fraction of one timeslot to complete, for a 10msec TTI, the initial power level would be set for only the firsttimeslot, and the remaining 14 timeslots would have the second value.For a 2 msec TTI, the initial power level would be set for the firsttimeslot, which is one-third of the TTI, and only the remainingtwo-thirds of the TTI would have the second power. It is not apparentthat such a method could be compliant, especially in the 2 msec TTIcase. Therefore, there exists a need for a method of determining CM orPAR to determine maximum-MPR and/or minimum-MPR, and ultimately thetransmit power, before the signal starts to be transmitted.

SUMMARY

A method and apparatus are provided for controlling transmit power withan estimated value of CM or PAR through estimation. The method may beapplied in determining a value for Maximum Power Reduction (MPR) for acomputing maximum-MPR or minimum-MPR by estimating CM or PAR from signalparameters, as opposed to directly measuring CM or PAR. The method ofestimating CM or PAR is applicable to any multicode signal.

BRIEF DESCRIPTION OF THE DRAWINGS

A more detailed understanding may be had from the following description,given by way of example and to be understood in conjunction with theaccompanying drawings wherein:

FIG. 1 is a functional block diagram of a Wireless Transmit Receive Unit(WTRU) in accordance with the present disclosure;

FIG. 2 is a block diagram of a simplified version of an off-lineprocessor;

FIG. 3 is a flow diagram of detailed version of an off-line initialconfiguration process;

FIG. 4 is a block diagram of a WTRU in accordance with an embodiment;

FIGS. 5A and 5B are two graphs, for the model in Equation 5 and themodel in Equation 6, respectively, depicting the distribution ofmaximum-MPR estimation errors;

FIGS. 6A and 6B are two graphs, for the model in Equation 5 and themodel in Equation 6, respectively, depicting the distribution of CMestimation errors;

FIGS. 7A and 7B are two graphs, for the model in Equation 5 and themodel in Equation 6, respectively, depicting the distribution of PARestimation errors; and

FIG. 8 is a flow diagram of a method of setting transmit power.

DETAILED DESCRIPTION

When referred to hereafter, the terminology “wireless transmit/receiveunit (WTRU)” includes but is not limited to a user equipment (UE), amobile station, a fixed or mobile subscriber unit, a pager, a cellulartelephone, a personal digital assistant (PDA), a computer or any othertype of user device capable of operating in a wireless environment.

When referred to hereafter, the terminology “base station” includes butis not limited to a Node-B, a site controller, an access point (AP), orany other type of interfacing device capable of operating in a wirelessenvironment.

FIG. 1 is a diagram of a WTRU 120 configured to perform the methoddisclosed hereinafter. In addition to components included in a typicalWTRU, the WTRU 120 includes a processor 125 configured to perform thedisclosed method, a receiver 126, which is in communication with theprocessor 125, a transmitter 127, which is in communication with theprocessor 125, an antenna 128 which is in communication with thereceiver 126 and the transmitter 127 to facilitate the transmission andreception of wireless data. The WTRU wirelessly communicates with a basestation 110.

A method of estimating transmit CM and/or PAR of a signal based on thesignal's configurable parameters, and applying the estimate to computethe MPR will now be described herein. Configurable parameters includethe number and type of physical channels and a configuration case. Aconfiguration case may be defined as a particular combination ofchannelization code and channel weight (termed β), preferably for bothcodes in-phase (I) and quadrature channel (Q) components. Channel weight(for a given service and data rate), the other parameters, referred tobelow as “configuration,” and all combinations of which may bedetermined based on requirements specified in 3GPP.

A signal may be defined as combination of a physical channel and a βterm. Each possible signal must be in at least one configuration case.The definition could be expanded. For example, it may include a subsetor limited range of some or all β terms for one or more of the physicalchannels comprising a configuration case. The identification of aminimal set of configuration cases which yields acceptably small CMand/or PAR estimation errors, which in turn drive the MPR estimationerror, is subjective.

An example set of eleven configuration cases is shown in Table 1. Theseconfiguration cases are limited to allowing up to one DPDCH. Those ofskill in the art would realize that the configuration cases need not beso limited. However, most likely they are suboptimal. Empirical resultsshown yield acceptably small estimation errors, specifically, that thelargest maximum-MPR estimation error is less than or equal to 1.5 dB.Table 1, shows that the configuration cases are defined by the threemain characteristics: 1) the maximum number of DPDCHs (Nmax DPDCH); 2)whether or not High Speed (HS) is enabled; and 3) the number andSpreading Factors (SFs) of E-DPDCHs (E-DPDCH codes @ SF). An alternativemapping is given in Table 2. Table 2, shows that dividing some of thecases originally defined in Table 1 into multiple cases results insmaller errors than the mapping of Table 1. Specifically, it shows thatthe largest maximum-MPR estimation error is less than or equal to 1.0dB.

Referring back to Table 1, the HS Chan Code column refers to thespecific “SF and an Orthogonal Variable (OV)SF code” used for theHS-DPCCH. Note that SF is always 256, and for OVSF one of two codes (33and 64) is used. This column is shown as ‘not applicable’ (N/A) when thethird column (i.e., HS) indicates there is no (‘N’) HS.

The E-DPDCH 1, 3 I or Q column indicates in which leg, I or Q, E-DPDCHchannels #1 and #3 appear, that is used in context of the columns.

The E-DPDCH 1, 3 Chan Code column refers to the SF and OVSF code for theE-DPDCH, if any, referred to as channel #1 and #3. For example,Configuration Case 6 has two E-DPDCHs, labeled as #1 and #3, the rest ofthe column either have none (not applicable) or one (by default “#1”).Most configuration cases have just one E-DPDCHs.

The E-DPDCH 2,4 Chan Code column is similar to the above, for the caseswhere there are two or more E-DPDCHs.

The I and Q columns show β values in the I and Q legs. In configurationcase 6, β_(ed) refers to E-DPDCH channels #1 and #2, and β_(ed3/4)refers to E-DPDCH channels #3 and #4.

TABLE 1 E-DPDCH E-DPDCH Config. Nmax E-DPDCH HS Chan E-DPDCH 1.3 Chan2.4 Chan Case DPDCH HS codes @ SF Code 1.3 I or Q Code Code I Q 0 1 N 0N/A N/A N/A N/A β_(d) β_(c) 1 0 Y 1@SF >= 4 256.33 I SF, SF/4 N/Aβ_(ec), β_(ed) β_(c), β_(hs) 2 0 N 1@SF >= 4 N/A I N/A N/A β_(ec),β_(ed) β_(c) 3 0 Y 0 256.33 I SF, SF/4 N/A N/A β_(c), β_(hs) 4 0 Y/N2@SF >= 4 256.33 I SF, SF/4 = 4.1 β_(ec), β_(ed) β_(c), β_(hs), 4.1β_(ed) 5 0 Y/N 2@SF >= 2 256.33 I 2.1 2.1 β_(ec), β_(ed) β_(c), β_(hs),β_(ed) 6 0 Y/N 4@SF = 256.33 I(#1) 2.1(#1) 2.1(#1) β_(ec), β_(ed),β_(c), β_(hs), 2/4/2/4 I(#3) 4.1(#3) 4.1(#3) β_(ed3/4) β_(ed), β_(ed3/4)7 1 Y 0, or 256.64 I SF, SF/2 N/A β_(d), β_(ec), β_(c), β_(hs) 1@SF >= 4β_(ed) 8 1 Y/N 2@SF = 4 256.64 if HS = “Y” SF, SF/2 = 4.2 β_(d), β_(ec),β_(c), β_(hs), then I 4.2 β_(ed) β_(ed) else Q 9 1 Y/N 2@SF = 2 256.64if HS = “Y” SF, SF/2 = 4.2 β_(d), β_(ec), β_(c), β_(hs), then I 4.2β_(ed) β_(ed) else Q 10 1 N 1@SF >= 4 N/A Q SF, SF/2 N/A β_(d), β_(ec)β_(c), β_(ed)

TABLE 2 Config. Nmax E-DPDCH Additional Case DPDCH HS codes@SFConditions 0 1 N 0 N/A 1 0 Y 1@SF >= 4 N/A 2 0 N 1@SF >= 4 N/A 3 0 Y 0N/A 4 0 Y/N 2@SF = 4 N/A 5 0 Y/N 2@SF = 2 N/A 6 0 Y/N 4@SF = N/A 2/4/2/4 7a 1 Y 0, or β_(c) − β_(d) > 0  7b 1@SF >= 4 β_(c) − β_(d) ≦ 0  8a 1Y/N 2@SF = 4 β_(c) − β_(d) > 0 15 * A_(ed) = 5  8b β_(c) − β_(d) > 015 * A_(ed) ~= 5  8c β_(c) − β_(d) ≦ 0 15 * A_(ed) = 5  8d β_(c) − β_(d)≦ 0 15 * A_(ed) ~= 5  9a 1 Y/N 2@SF = 2 β_(c) − β_(d) > 0  9b β_(c) −β_(d) ≦ 0 15 * A_(ed) = 5  9c β_(c) − β_(d) ≦ 0 15 * A_(ed) ~= 5 10  1 N1@SF >= 4 N/A

Configuration case 0 in Table 1 and Table 2 is a trivial case known torequire zero maximum-MPR. For this configuration case, the computationalmethod for all other configuration cases should not be applied; rather,maximum-MPR and/or minimum-MPR should simply be set to zero.

Referring to FIG. 2, a simplified version of an off-line process 200 isshown. As will be explained in greater detail hereinafter with respectto FIG. 3, the process 200 ultimately computes and stores parameters foruse by a WTRU to generate maximum-MPR and/or minimum-MPR values. InUMTS, each combination of a physical channel parameter and a quantizedterm β is a possible signal. The quantized term is based on aconfiguration of the signal. First, all possible signals are mapped intoset of configuration cases (210). Using information given in the tworightmost columns of Table 1 (I and Q), the quantized terms aregenerated for all possible signals (220). The CM and/or PAR for allpossible signals are measured by a transmitter simulation (230). Themeasurement of CM and PAR will be described in greater detailhereinafter.

The pre-computed terms α are preferably determined 240 using the outputof the transmitter simulation 230. One set of α terms, computed based onEquation 7 hereinafter, for CM and/or one set for PAR is preferablydetermined for each configuration case defined above.

For each configuration case, the transmitter simulation 230 measures CMand PAR for all possible signals, (the mathematical derivation ofestimating CM and/or PAR is derived in detail hereinafter), here definedas all possible combinations of quantized β terms 220 per 3GPP. Thetechnique of least square fitting may be used to determine the values ofpre-computed terms α for a specific configuration case, either from allpossible signals of the configuration case or from a sample subsetthereof. The computed α terms, the configuration case, and computedadjustment factors are calculated (240). These values are later builtinto a WTRU 400 via firmware, software or hardware.

FIG. 3 is a flow diagram of an off-line initialization configurationprocess (300). The process 300 computes the α terms for both CM and PAR,and determines the adjustment factors for a configuration case. Thesevalues are stored in a WTRU (400) for a given signal's estimates CM andPAR.

Referring to FIG. 3, a detailed version of an off-line process 300 isshown. First, a configuration case according to a characteristic of aphysical channel is defined at 310. For example, illustrated as theconfiguration case 9 in Table 1, DPCCH, one DPDCH (maximum of oneDPDCH), HS-DPCCH (Δ_(ACK) and Δ_(CQI) set identically; positiveacknowledgement (ACK) and channel quality indication (CQI) alwaystransmitted), E-DPCCH and 2@SF=2 (two E-DPDCHs at SF equal to two) isdefined.

The required individual, squared, and intra-component cross β terms aredetermined (320), using the information given in the two rightmostcolumns of Table 1 (I and Q). From the notation of Equation 5 (describedhereinafter), {β_(I1) β_(I2) β_(I3)}={β_(d) β_(ec) β_(ed)} and {β_(Q1)β_(Q2) β_(Q3)}={β_(c) β_(hs) β_(ec)} (the particular numericaldesignations are arbitrary). There are sixteen such terms defined inTable 3: β_(ec), β_(ed), β_(d), β_(c), β_(hs), β_(ec) ², β_(ed) ², β_(d)², β_(c) ², β_(hs) ², β_(ec)β_(ed), β_(ec)β_(d), β_(ed)β_(d),β_(c)β_(hs), β_(c)β_(ed), and β_(hs)β_(ed).

All possible signals, (i.e., all combinations of the quantized β termsfor the channels) of the configuration case are then determined (320).Per 3GPP, there are implicitly thirty combinations of paired values ofβ_(c) and β_(d), explicitly nine values of A_(hs)=β_(hs)/β_(c), ninevalues of A_(ec)=β_(ec)/β_(c) and thirty values of A_(ed)=β_(ed)/β_(c),or 72,900 possible signal combinations for each configuration case intotal. The 72,900 combinations are not listed here.

A transmitter simulation is used to measure CM and measure 99% PAR forall 72,900 possible signals in each configuration case (330). The145,800 measured values are not listed here.

Using the 72,900 possible signals in each configuration case and theirmeasured values of linear CM and linear PAR, the sixteen pre-computed αvalues for estimating CM and the sixteen pre-computed α values forestimating PAR are calculated (340) using Equation 7. The symbolic termsare given in Equations 8 through Equations 11; the numerical values ofthe α terms are given in Table 3. Although only a small subset of the72,900 combinations can be used, given that the matrix X with 72,900rows is needed in the next step, the full set of 72,900 combinations isused for computation of Table 3 of one configuration case.

TABLE 3 Function of β terms α_(CM) α_(PAR) β_(ec) −1.53154 −0.0333305β_(ed) −1.04303 +1.97253 β_(d) −1.88422 −0.691914 β_(c) −1.10666−1.24791 β_(hs) −0.851261 −0.642072 β_(ec) ² +2.7545 +2.3413 β_(ed) ²+3.39477 +1.35334 β_(d) ² +2.85157 +2.61758 β_(c) ² +2.47229 +2.86022β_(hs) ² +2.37892 +2.63543 β_(ec) β_(ed) +2.33816 +1.72716 β_(ec) β_(d)+2.18533 +1.27585 β_(ed) β_(d) +2.95673 +2.75154 β_(c) β_(hs) +1.75287+1.80679 β_(c) β_(ed) +2.05286 +3.06353 β_(hs) β_(ed) +1.59968 +2.07734

For each of the possible signals, linear CM and linear PAR are estimated(350) using the model described by Equations 5 and 6 (describedhereinafter). The computation, in matrix form, is given in Equation 12.Matrix X is the numerator of Equation 5 and includes the normalizationfunction for the single β terms. Matrix Y is the linear CM and linearPAR measurements multiplied by the denominator of Equation 5; a similarform is used for the model of Equation 6.

The estimation errors for both CM and PAR are preferably calculated(360) using Equation 13. To illustrate further, the distribution of theCM estimation errors (in dB) is given in FIGS. 6A and 6B. Thedistribution of the PAR estimation errors, in dB, is given in FIGS. 7Aand 7B. FIGS. 6A and 7A represent the model described in Equation 5.Whereas, FIGS. 6B and 7B represent the model described in Equation 6.

The requisite adjustment factors are the determined (370). By inspectionit can be seen that for the model of Equation 5, the adjustment factorfor maximum-MPR, the largest magnitude positive error in FIG. 6A, isapproximately 0.54 dB or 1/0.883. If minimum-MPR was the desired result,the adjustment factor for minimum-MPR using CM, the largest magnitudenegative error in FIG. 6A, is approximately −0.71 dB. The adjustmentfactor for minimum-MPR using PAR, the largest magnitude negative errorin FIG. 7A, is approximately −0.41 dB. The corresponding values for themodel of Equation 6, obtained by inspection of FIG. 6B and FIG. 7B, are0.54 dB, −0.80 dB, and −0.57 dB.

The distribution of maximum-MPR error is determined (380), by applyingthe adjustment factors, coincidentally both being 0.54 dB, as computedabove.

Inspection of FIG. 5A and FIG. 5B, the distributions of maximum-MPRerror, shows that for both models, the maximum of the maximum-MPR erroris 1.5 dB, if this is deemed sufficiently small, (which in this exampleit is) nominally either model can be used.

As secondary criteria, it should be noted that the frequency ofoccurrence of the maximum error for the model of Equation 5,specifically 9/72,900, is lower than that of the model of Equation 6,specifically, 406/72,900, as illustrated in FIGS. 5A and 5B. Thus themodel of Equation 5 is chosen and its α values and adjustment factor(390) are configured in the WTRU (400). Alternatively, the model ofEquation 6 requires fewer multiplications to estimate CM, and that modelcould have been chosen if that is a significant factor.

The derivation of estimating CM and/or PAR will now be described. ThePAR of the uplink signal after the channel weights have been applied,but before the root raised cosine (RRC) and other filters are applied,is determined in accordance with Equation 2.

$\begin{matrix}{{{PAR} = {{10{\log \left( {PAR}_{linear} \right)}} = {10{\log\left( \frac{\left( {\sum\limits_{j = 1}^{N_{I}}\beta_{Ij}} \right)^{2} + \left( {\sum\limits_{j = 1}^{N_{Q}}\beta_{Qj}} \right)^{2}}{{\sum\limits_{j = 1}^{N_{I}}\beta_{Ij}^{2}} + {\sum\limits_{j = 1}^{N_{Q}}\beta_{Qj}^{2}}} \right)}}}};} & {{Equation}\mspace{14mu} (2)}\end{matrix}$

where;

β_(I) is the channel weight for a physical channel in the I component;

-   -   β_(Q) is the channel weight for a physical channel in the Q        component;

N_(I) is the number of physical channels in the I component; and

N_(Q) is the number of physical channels in the Q component.

In accordance with one embodiment, for a given configuration case,CM_(linear), (CM in linear—not dB—form, and without the 0.5 dBquantization of the method of 3GPP), is preferably estimated as afunction related to the pre-filter PAR_(linear) of Equation 2, as perEquation 3:

$\begin{matrix}{{{CM}_{linear} \approx \frac{\begin{matrix}{{\sum\limits_{n = 1}^{N_{Order}}\left( {{f_{nrm}\left( {\overset{\_}{\beta},n} \right)}{\sum\limits_{j = 1}^{N_{I}}{\gamma_{j}\beta_{Ij}}}} \right)^{n}} +} \\{\sum\limits_{n = 1}^{N_{Order}}\left( {{f_{nrm}\left( {\overset{\_}{\beta},n} \right)}{\sum\limits_{j = 1}^{N_{Q}}{\gamma_{j}\beta_{Qj}}}} \right)^{n}}\end{matrix}}{{\sum\limits_{j = 1}^{N_{I}}\beta_{Ij}^{2}} + {\sum\limits_{j = 1}^{N_{Q}}\beta_{Qj}^{2}}}};} & {{Equation}\mspace{14mu} (3)}\end{matrix}$

where;

γ_(j) are real weighting factors for each physical channel;

n is an integer that defines an index of a summation;

N_(Order) is an arbitrary polynomial order; and

${f_{nrm}\left( {\overset{\_}{\beta},n} \right)} = \left( {{\sum\limits_{j = 1}^{N_{I}}\beta_{Ij}^{2}} + {\sum\limits_{j = 1}^{N_{Q}}\beta_{Qj}^{2}}} \right)^{\frac{2 - n}{2}}$

is a normalization function that makes the values of γ_(j) independentof arbitrary scaling of the β terms.

PAR_(linear) at the output of the filters may also be estimated usingthe same function as in Equation 3, with only the values of the γ termsbeing different from those for CM_(linear). For any possible signal of agiven configuration case, using Equation 3 to estimate CM_(linear),PAR_(linear) will generally result in a difference between estimatedvalues and measured values; this is referred to as the estimation error.

While N_(Order) may be selected as any positive integer, in oneembodiment, for example, it is N_(Order)=2. Empirical results show thatby using N_(Order)=2, the range of estimation errors for all possiblesignals is acceptably small for determining maximum-MPR and minimum-MPR.Thus, selecting N_(Order) greater than 2 results in additionalcomplexity but no vital performance improvement. Accordingly, Equation 3is simplified when N_(Order) is set to 2 as shown in Equation 4:

$\begin{matrix}{{CM}_{linear} \approx {\frac{\begin{matrix}{\left( {\sum\limits_{j = 1}^{N_{I}}{\gamma_{j}\beta_{Ij}}} \right)^{2} + \left( {\sum\limits_{j = 1}^{N_{Q}}{\gamma_{j}\beta_{Qj}}} \right)^{2} +} \\{\sqrt{{\sum\limits_{j = 1}^{N_{I}}\beta_{Ij}^{2}} + {\sum\limits_{j = 1}^{N_{Q}}\beta_{Qj}^{2}}}\left( {{\sum\limits_{j = 1}^{N_{I}}{\gamma_{j}\beta_{Ij}}} + {\sum\limits_{j = 1}^{N_{Q}}{\gamma_{j}\beta_{Qj}}}} \right)}\end{matrix}}{{\sum\limits_{j = 1}^{N_{I}}\beta_{Ij}^{2}} + {\sum\limits_{j = 1}^{N_{Q}}\beta_{Qj}^{2}}}.}} & {{Equation}\mspace{14mu} (4)}\end{matrix}$

Expanding Equation 4 yields Equation 5.

$\begin{matrix}{{CM}_{linear} \approx \frac{\begin{matrix}{{\sum\limits_{j = 1}^{N_{I}}{\alpha_{j}\beta_{Ij}^{2}}} + {\sum\limits_{j = 1}^{N_{I}}{\sum\limits_{k = {j + 1}}^{N_{I}}{\alpha_{j,k}\beta_{Ij}\beta_{Ik}}}} +} \\{{\sum\limits_{j = 1}^{N_{Q}}{\alpha_{j}\beta_{Qj}^{2}}} + {\sum\limits_{j = 1}^{N_{Q}}{\sum\limits_{k = {j + 1}}^{N_{Q}}{\alpha_{j,k}\beta_{Qj}\beta_{Qk}}}} +} \\{\sqrt{{\sum\limits_{j = 1}^{N_{I}}\beta_{Ij}^{2}} + {\sum\limits_{j = 1}^{N_{Q}}\beta_{Qj}^{2}}}\left( {{\sum\limits_{j = 1}^{N_{I}}{\alpha_{j}\beta_{Ij}}} + {\sum\limits_{j = 1}^{N_{Q}}{\alpha_{j}\beta_{Qj}}}} \right)}\end{matrix}}{{\sum\limits_{j = 1}^{N_{I}}\beta_{Ij}^{2}} + {\sum\limits_{j = 1}^{N_{Q}}\beta_{Qj}^{2}}}} & {{Equation}\mspace{14mu} (5)}\end{matrix}$

which expresses CM_(linear) as being approximately equal to a weightedversion of the inner product of squared weighted-individual (weighted bythe square root term), intra-component cross β terms, along with the αterms that are still unknown. The formulation similarly applies toPAR_(linear), with only the values of the α terms being different.

An alternative model to that specified in Equation 5 is shown inEquation 6. Equation 6 model eliminates the single β terms and theassociated normalization function (the last term in the numerator ofEquation 5). The empirical results show that for some configurationcases this model yields smaller estimation errors than the model ofEquation 5.

$\begin{matrix}{{CM}_{linear} \approx \frac{\begin{matrix}{{\sum\limits_{j = 1}^{N_{I}}{\alpha_{j}\beta_{Ij}^{2}}} + {\sum\limits_{j = 1}^{N_{I}}{\sum\limits_{k = {j + 1}}^{N_{I}}{\alpha_{j,k}\beta_{Ij}\beta_{Ik}}}} +} \\{{\sum\limits_{j = 1}^{N_{Q}}{\alpha_{j}\beta_{Qj}^{2}}} + {\sum\limits_{j = 1}^{N_{Q}}{\sum\limits_{k = {j + 1}}^{N_{Q}}{\alpha_{j,k}\beta_{Qj}\beta_{Qk}}}}}\end{matrix}}{{\sum\limits_{j = 1}^{N_{I}}\beta_{Ij}^{2}} + {\sum\limits_{j = 1}^{N_{Q}}\beta_{Qj}^{2}}}} & {{Equation}\mspace{14mu} (6)}\end{matrix}$

For a given configuration case, the values of the α terms can bedetermined by: 1) using a transmitter simulation (230) to measureCM_(linear) and/or PAR_(linear) for all, or a reduced set of exemplarpossible signals; and 2) employing the well known method of leastsquares fitting, which is given in matrix form in Equation 7:

α=(X ^(T) X)⁻¹ X ^(T) Y  Equation (7)

where;

-   -   X is a matrix (known as a design or Vandermode matrix) with one        row for each signal, in which each element of a row is the        numerical value of a squared, weighted individual or        intra-component cross β term. These are all determined by        replacing the β_(I) and β_(Q) terms of the numerator of Equation        5 or Equation 6 with specific channel β terms; for the case of        two or four E-DPDCHs, the individual and squared β_(ed) terms        should each occupy only one row of X, not two or four; and    -   Y is a column vector with one element for each signal, in which        each element is the measured CM_(linear) or PAR_(linear),        respectively. Given that the α terms for estimating CM or the α        terms for estimating PAR are to be calculated, multiplied by the        signal's weighting factor in the denominator of Equation 5 or        Equation 6. Alternatively, Y can be a matrix of two such        columns, one for CM_(linear) and another for PAR_(linear), given        that α terms for estimating both CM and PAR are to be        calculated.

The symbolic terms (not their numerical values) used in Equation (7) tocompute the α values for this example are provided as:

$\begin{matrix}{\overset{->}{L} = \begin{bmatrix}{\beta_{d\; 1}^{2} + \beta_{{ec}\; 1}^{2} + {2\; \beta_{{ed}\; 1}^{2}} + \beta_{c\; 1}^{2} + \beta_{{hs}\; 1}^{2}} \\{\beta_{d\; 2}^{2} + \beta_{{ec}\; 2}^{2} + {2\beta_{{ed}\; 2}^{2}} + \beta_{c\; 2}^{2} + \beta_{{hs}\; 2}^{2}} \\\vdots \\{\beta_{d\; 72900}^{2} + \beta_{{ec}\; 72900}^{2} + {2\beta_{{ed}\; 72900}^{2}} + \beta_{c\; 72900}^{2} + \beta_{{hs}\; 72900}^{2}}\end{bmatrix}} & {{Equation}\mspace{14mu} (8)} \\{\sqrt{\overset{->}{L}} = {\quad\begin{bmatrix}\sqrt{\beta_{d\; 1}^{2} + \beta_{{ec}\; 1}^{2} + {2\; \beta_{{ed}\; 1}^{2}} + \beta_{c\; 1}^{2} + \beta_{{hs}\; 1}^{2}} \\\sqrt{\beta_{d\; 2}^{2} + \beta_{{ec}\; 2}^{2} + {2\beta_{{ed}\; 2}^{2}} + \beta_{c\; 2}^{2} + \beta_{{hs}\; 2}^{2}} \\\vdots \\\sqrt{\beta_{d\; 72900}^{2} + \beta_{{ec}\; 72900}^{2} + {2\beta_{{ed}\; 72900}^{2}} + \beta_{c\; 72900}^{2} + \beta_{{hs}\; 72900}^{2}}\end{bmatrix}}} & {{Equation}\mspace{14mu} (9)} \\{X^{T} = \begin{bmatrix}{\begin{bmatrix}\beta_{{ec}\; 1} & \beta_{{ec}\; 2} & \ldots & \beta_{{ec}\; 72900} \\\beta_{{ed}\; 1} & \beta_{{ed}\; 2} & \ldots & \beta_{{ed}\; 72900} \\\beta_{d\; 1} & \beta_{d\; 2} & \ldots & \beta_{d\; 72900} \\\beta_{c\; 1} & \beta_{c\; 2} & \ldots & \beta_{c\; 72900} \\\beta_{{hs}\; 1} & \beta_{{hs}\; 2} & \ldots & \beta_{{hs}\; 72900}\end{bmatrix} \cdot {{diag}\left( \sqrt{\overset{->}{L}} \right)}} \\\begin{bmatrix}\beta_{{ec}\; 1}^{2} & \beta_{{ec}\; 2}^{2} & \ldots & \beta_{{ec}\; 72900}^{2} \\\beta_{{ed}\; 1}^{2} & \beta_{{ed}\; 2}^{2} & \ldots & \beta_{{ed}\; 72900}^{2} \\\beta_{d\; 1}^{2} & \beta_{d\; 2}^{2} & \ldots & \beta_{d\; 72900}^{2} \\\beta_{c\; 1}^{2} & \beta_{c\; 2}^{2} & \ldots & \beta_{c\; 72900}^{2} \\\beta_{{hs}\; 1}^{2} & \beta_{{hs}\; 2}^{2} & \ldots & \beta_{{hs}\; 72900}^{2} \\{\beta_{{ec}\; 1}\beta_{{ed}\; 1}} & {\beta_{{ec}\; 2}\beta_{{ed}\; 2}} & \ldots & {\beta_{{ec}\; 72900}\beta_{{ed}\; 72900}} \\{\beta_{{ec}\; 1}\beta_{d\; 1}} & {\beta_{{ec}\; 2}\beta_{d\; 2}} & \ldots & {\beta_{{ec}\; 72900}\beta_{d\; 72900}} \\{\beta_{{ed}\; 1}\beta_{d\; 1}} & {\beta_{{ed}\; 2}\beta_{d\; 2}} & \ldots & {\beta_{{ed}\; 72900}\beta_{d\; 72900}} \\{\beta_{c\; 1}\beta_{{hs}\; 1}} & {\beta_{c\; 2}\beta_{{hs}\; 2}} & \ldots & {\beta_{c\; 72900}\beta_{{hs}\; 72900}} \\{\beta_{c\; 1}\beta_{{ed}\; 1}} & {\beta_{c\; 2}\beta_{{ed}\; 2}} & \ldots & {\beta_{c\; 72900}\beta_{{ed}\; 72900}} \\{\beta_{{hs}\; 1}\beta_{{ed}\; 1}} & {\beta_{{hs}\; 2}\beta_{{ed}\; 2}} & \ldots & {\beta_{{hs}\; 72900}\beta_{{ed}\; 72900}}\end{bmatrix}\end{bmatrix}} & {{Equation}\mspace{14mu} (10)} \\{Y = {{{diag}\left( \overset{->}{L} \right)} \cdot \begin{bmatrix}{CM\_ linear}_{1} & {PAR\_ linear}_{1} \\{CM\_ linear}_{2} & {PAR\_ linear}_{2} \\\vdots & \vdots \\{CM\_ linear}_{72900} & {CM\_ linear}_{72900}\end{bmatrix}}} & {{Equation}\mspace{14mu} (11)} \\{\hat{Y} = {X \cdot \alpha}} & {{Equation}\mspace{14mu} (12)} \\{{err} = {{10{\log \left( \hat{Y} \right)}} - {10{\log (Y)}}}} & {{Equation}\mspace{14mu} (13)}\end{matrix}$

The reduced set of possible signals cited above by example refers to thefact that the number of signals necessary to reliably compute the αterms can be orders-of-magnitude less than the number of all possiblesignals. However, the matrix X with all possible signals is used tocalculate the estimation errors using Equations 12 and 13. There is novital economy realized in the off-line processor 200 by limiting thenumber of signals in X to compute the α terms.

The weighting factors specified in Equations 5 and 6, which are used toconstruct the matrices Y and X, respectively, the digital power (thedenominator of Equation 5 and Equation 6), and root mean squaremagnitude (the square root term in the numerator of Equation 5) of eachsignal may, in certain implementations be equivalent or nearlyequivalent for all signals. In such case it may not need to be computedfor each signal. Instead, the two weighting factors can each be theirconstant values that are common to all signals.

The weighting factors could also be eliminated from Equations 5 and 6,and be effectively incorporated into the α terms, if the scaling of thedigital β terms in the transmitter simulation used to measure CM and/orPAR and subsequently calculate the α terms is identical to the scalingof the digital β terms in the WTRU.

Using the process of FIGS. 2 and 3, the α terms for all of the definedconfiguration cases, an adjustment factor for each configuration case,and the model which minimizes the maximum-MPR or minimum-MPR estimationerror have been computed for the two models described in Equations 5 and6. The model which minimizes the maximum-MPR or minimum-MPR estimationerror is computed as follows:

For the case of maximum-MPR, there are three alternatives fordetermining the model that minimizes the maximum-MPR estimation error.

The first alternative is that the estimated CM_(linear) from Equations 5or 6 should be adjusted such that an adjusted estimated CM can not begreater than the value which would have been obtained from actualmeasurement of CM. The adjustment factor should be the largest magnitudepositive error for the particular configuration case; it should ineffect be subtracted from the actual estimate. The intent of adjustingthe estimate in this manner is to prevent overestimating CM for anysignal.

The second alternative is that the estimated CM_(linear) from Equations5 or 6 should be adjusted such that the maximum-MPR determined from anadjusted estimated CM can not be greater than the maximum-MPR whichwould have been obtained from actual measurement of CM. The intent ofadjusting the estimate in this manner is to prevent overestimatingmaximum-MPR for any signal. The method of determining the adjustmentfactor follows.

1) For each signal in the configuration, determine the estimated MPRusing the estimated CM and determine the true MPR from the knownsimulated true CM.

2) Compute MPR error as per Equation 14:

MPR_error=MPR_true−MPR_estimated  Equation (14)

3) From amongst the signals with MPR error less than zero, select as theraw adjustment factor per Equation 15:

adjustment_factor_raw=max(CM_estimated−ceil(CM_true,0.5));  Equation(15)

where, ceil (•,0.5) means rounding upwards to the closest 0.5.

4) The final adjustment value is the value from Equation 15 plus a smallquantity, ε, which ensures that for the signal with the maximumCM_estimated in Equation 15 does not get rounded up to the next 0.5 dBafter applying the adjustment factor. In other words, the adjustmentfactor is calculated using Equation 16, in which the maximum is selectedfrom amongst the signals with MPR error that is less than zero.

adjustment_factor=max(CM_estimated−ceil(CM_true,0.5))+ε  Equation (16)

The third alternative is that a smaller-magnitude adjustment factor thanthose per the other alternatives be applied, the amount selected as adesign trade-off, (e.g., preventing overestimation of CM for onlyparticular signals of a configuration case).

For the case of computing minimum-MPR for determining the model thatminimizes the minimum-MPR estimation error, the estimated CM or PARshould be adjusted such that an adjusted estimated CM or PAR can not beless than the value of actual measurement of CM or PAR. The adjustmentfactor should be the largest magnitude negative CM or PAR estimationerror for the particular configuration case; it should in effect besubtracted from the actual estimate. The intent of applying theadjustment factor in this manner is to prevent underestimating CM or PARfor any signal. Alternatively, a smaller-magnitude negative adjustmentfactor could be applied, the amount selected as a design trade-off,(e.g., preventing underestimation of CM or PAR for only particularsignals of a configuration case).

For each configuration case, after the application of the adjustmentfactor by either method, an evaluation must be made as to whether theerrors are sufficiently small for both models. Examples of thedistribution of measurement errors for a particular configuration caseare given in FIGS. 5A, 5B, 6A, 6B, 7A and 7B. FIGS. 5A, 6A, and 7Arepresent the model described in Equation 5; and, FIGS. 5B, 6B, and 7Brepresent the model described in Equation 6. FIGS. 5A and 5B show thedistribution of maximum-MPR estimation errors for a particular case. InFIGS. 5A and 5B, due to the ceil operation in the computation ofmaximum-MPR, the distribution is highly quantized.

FIGS. 6A and 6B show the density of CM estimation errors; FIG. 6A has anarrower density compare to FIG. 6B. The distributions in FIGS. 6A and6B, and FIGS. 7A and 7B are essentially continuous. FIGS. 7A and 7B showthe density of the errors of estimating PAR. For computing maximum-MPR,the maximum maximum-MPR error should be within a desired limit.Alternatively, the difference between the extreme positive and negativeCM measurement errors being within a desired limit could be thecriteria. However, using the maximum maximum-MPR error is preferable.For computing minimum-MPR using CM or PAR, the difference between theextreme positive and negative measurement errors should be within adesired limit.

For the maximum-MPR, applying the adjustment factor per the firstalternative results in no signal having overestimated MPR and somesignals having underestimated MPR. Applying the adjustment factor perthe second alternative results in no signal having overestimated CM andsome signals having underestimated CM. Specifically, the signal with thelargest positive CM error will have CM correctly estimated, the signalwith the largest magnitude negative CM error will have CM underestimatedby the difference between the largest magnitude positive and negative CMerrors, and other signals will have CM underestimated by some lesseramount.

For minimum-MPR, applying the adjustment factor results in no signalhaving underestimated CM or PAR; and some signals having overestimatedCM or PAR. Specifically, the signal with the largest positive CM or PARerror will have CM or PAR correctly estimated; the signal with thelargest magnitude positive CM or PAR error will have CM or PARoverestimated by the difference between the largest magnitude positiveand negative CM errors.

There are two potential problems with estimation errors: First, due tothe deliberate underestimation and overestimation of CM and PAR,calculated minimum-MPR may exceed calculated maximum-MPR. In which case,the WTRU may not select a value of MPR that would ensure compliance withboth the MPR and ACLR requirements of a standard, for example, 3GPP.Second, the larger the difference of largest magnitude positive andnegative estimation errors, the greater is the difference betweenminimum-MPR obtained per the method and minimum-MPR hypotheticallyobtainable by measurement, reducing the highest achievable maximumtransmit power.

Two possible remedies for these issues are: 1) the tradeoff describedabove could be applied by selecting alternative adjustment factor valuessuch that for some presumably small set of signals the computed MPR isnot compliant; and 2) the particular configuration case could be brokenin two or more configuration cases, with the goal that the resultingestimation errors would be smaller. For example, if analysis revealedthat the largest estimation errors occur for the largest β values for aparticular physical channel, a separate configuration case could becreated with only those β values.

Once a set of configuration cases has been defined and the α terms andadjustment factors for all of the configuration cases have beencomputed, they are preferably stored in a table in a WTRU.

Referring to FIG. 4, a WTRU 400 is shown. Prior to the start oftransmission of each TTI, an appropriate configuration case is selectedgiven the data supplied by Medium Access Control (MAC) layer of thetransport block. For the definition of the set of configuration casesgiven in Table 1, selection would be per the mix of physical channels tobe used to transmit the transport block, and possibly the E-DPDCHspreading factor.

Whether the MPR computation device (430) computes maximum-MPR,minimum-MPR or both, and, if the device computes minimum-MPR using PAR,CM_(linear) is estimated per Equation 17, a simplified version ofEquation 5 and Equation 6:

$\begin{matrix}{{{CM}_{linear} \approx \frac{N}{D}};} & {{Equation}\mspace{14mu} (17)}\end{matrix}$

where N and D are a numerator and a denominator of Equation 5 orEquation 6, respectively, using the CM α terms of the configuration casedetermined hereinabove. PAR_(linear) is estimated using Equation 11, butreplacing CM_(linear) with PAR_(linear) and using the PAR α terms of theconfiguration case. CM_(linear) and/or PAR_(linear) is then converted todB form.

If the MPR computation device (430) computes maximum-MPR, the adjustmentfactor (in dB) selected for computing maximum-MPR is subtracted from theestimate of CM in dB. This gives the value of CM that is used to computemaximum-MPR.

If the MPR computation device (430) computes minimum-MPR using CM, theadjustment factor (in dB) selected for computing minimum-MPR using CM issubtracted from the estimate of CM in dB. This results in using thevalue of CM to compute the minimum-MPR.

If the MPR computation device (430) computes minimum-MPR using PAR, theadjustment factor (in dB) selected for computing minimum-MPR using PARis subtracted from the estimate of PAR in dB and the result is used tocompute minimum-MPR.

If the MPR computation device computes maximum-MPR, maximum-MPR ispreferably computed per 3GPP. If the MPR computation device computesminimum-MPR, minimum-MPR is preferably computed per the poweramplifier's specification.

A device computing either maximum-MPR or minimum-MPR, but not both,would output the computed maximum-MPR or minimum-MPR as the value of MPRused to set transmit power. A device computing both maximum-MPR andminimum-MPR could choose some intermediate value as the value of MPRused to set transmit power and remain compliant to the standard and tothe manufacturer's recommendation.

It is not necessary to actually fully estimate the value of CM, but onlydetect if the estimated value of CM is above or below one or morethreshold values. One possible threshold test that has the advantage ofavoiding the divide operation in Equation 17 can be made by slightlymodifying Equation 17 as provided in Equation 18.

CM_(linearT) ·D

N  Equation (18)

where; CM_(linearT) is a particular threshold value of CM_(linear); the

operator is a threshold test indicating that CM_(linear) is greater thanCM_(linearT) if the inequality is “true”.

The efficient algorithm given in C language notation setting the valuesof max_MPR_dB and the threshold values which are shown in Table 4 arederived from Table 6.1A of 3GPP TS 25.101. The values of linearequivalents of the adjustment factors are selected for computation ofmaximum-MPR.

TABLE 4 Index CM_linear_T MPR_dB 0 10{circumflex over ( )}(1.0/10) =1.258925 Not Used 1 10{circumflex over ( )}(1.5/10) = 1.412538 0.5 210{circumflex over ( )}(2.0/10) = 1.584893 1.0 3 10{circumflex over( )}(2.5/10) = 1.778279 1.5 4 10{circumflex over ( )}(3.0/10) = 1.9952622.0 5 Not Used 2.5

A device-specific method for computing minimum-MPR is likely to computesimilarly to maximum-MPR based on some number, possibly only one,threshold values of CM and/or PAR, and a similar algorithm can be usedto compute it.

Referring back to FIG. 4, which is a WTRU 400 configured for use forwireless communication, digital user data and control data are receivedand processed by the scaling circuitry 450 to digitally scale the datato set their relative transmit powers. The digital user data may beencoded into channels such as in dedicated physical data channels(DPDCHs) or in enhanced DPDCHs (E-DPDCHs). The control data may beencoded into channels such as in dedicated physical control channels(DPCCHs), high speed DPCCHs (HS-DPCCHs) or enhanced DPCCHs (E-DPCCHs).The scaling circuitry 450 operates on these respective channels.

The scaled data is filtered by filtering devices 460 and this filtereddata is converted to an analog signal by a digital-to-analog converter(DAC) 470 and transmitted by the radio transmitter 480 through theantenna (Tx) 490. The WTRU's transmitter has adjustable, (i.e., powercontrollable), overall transmit power as well as scalable individualchannel inputs, as represented in FIG. 4 by the analog gains valuenumber and digital gains value number, respectively. Other variations ofa controllable transmission device may be employed.

The transmit power of the individual channels and the total transmitpower are set based upon the procedures specified in 3GPP by thetransmit power control unit 440. The nominal maximum transmit power isdetermined by the WTRU power class or by the network. Maximum transmitpower for the WTRU power classes are those as specified in 3GPP. TheWTRU may autonomously limit its maximum transmit power by maximum-MPR, avalue within the limits defined in 3GPP, or by a lesser device-specificminimum-MPR.

The transmit power control unit 440 sets the transmit power usingmultiple parameters. One of these parameters is MPR. To computer theMPR, first a configuration case is identified based on the off-lineconfiguration parameters obtained as described hereinabove with respectto FIGS. 2 and 3 (410). For the identified case, adjusted estimated CMand/or PAR is computed (420) as described hereinafter.

An MPR is set based on a value for maximum-MPR and/or minimum-MPR (430).The maximum-MPR and/or minimum-MPR is preferably computed by aprocessing device 430 based on an adjusted estimate of CM and/or PAR(420), or an adjusted estimate to MPR. If it is computed based on theadjustment to MPR then there are no adjustments made to CM and/or PAR.

The WTRU 400 can be configured to compute either or both MPRs; and tocompute minimum-MPR from either CM or PAR such that the use of any suchcombination is selectable. The estimation of CM and/or PAR may be afunction of pre-computed terms, denoted as α terms, and functions of thedesired relative channel powers (β terms) of the transmitted signal,where the particular functions of the β terms are based on the certainphysical parameters of the signal. The adjustment of the estimate may befrom a pre-computed term.

To compute either or both MPRs in the WTRU 400, First, for a TTI, thesignal is of the example configuration, with channel weights from MAC-esbeing β_(c)=15, β_(d)=6, A_(hs)=β_(hs)/β_(c)=max (Δ_(ACK) andΔ_(CQI))=15/15, A_(ec)=β_(ec)/β_(c)=15/15, A_(ed)=β_(ed)/β_(c)=95/15.This example signal is signal U in R4-060176, 3GPP TSG RAN 4 Meeting#38.

Second, using digital scaling the WTRU 400 computes the followingdigital channel weights: β_(c)=22, β_(d)=9, β_(hs)=22, β_(ec)=22,β_(ed)=200. These weights are in the desired proportion to each otherand the sum of their squares is a desired constant value.

Third, using the α_(CM) and β terms in Table 3, the digital channelweights and the estimate of CM_(linear) are computed using Equation 5 as1.0589, equivalent to 0.2487 dB.

Fourth, the estimate of CM is adjusted by subtracting 0.54 dB, yieldingapproximately −0.29 dB. Alternatively, in linear form, the estimate isadjusted by multiplying 1.0589 by 0.883, yielding approximately 0.93.

Fifth, the linear adjusted estimate of CM, 0.94, is less than the firstlinear threshold in Table 4; hence, maximum-MPR is computed as 0 dB.

To summarize by referring to FIG. 8, a procedure 800 for settingtransmit power by computing MPR in the WTRU 400 is shown. Per aconfiguration case adjustment factors and the pre-computed α values aredetermined and processed in an off-line processor (810). These valuesare stored in the WTRU 400 to help the WTRU 400 identify a configurationcase (820). Once a configuration case is determined, adjusted estimatedCM and/or PAR are computed (830). Using these adjusted estimated values,maximum-MPR and/or minimum-MPR are computed (840), and an MPR is set.The MPR, nominal maximum power, and the Power Control Commands arecombined (850) and the transmit power is set (860).

Although the features and elements are described in the embodiments inparticular combinations, each feature or element can be used alonewithout the other features and elements or in various combinations withor without other features and elements. The methods or flow chartsprovided may be implemented in a computer program, software, or firmwaretangibly embodied in a computer-readable storage medium for execution bya general purpose computer or a processor. Examples of computer-readablestorage mediums include a read only memory (ROM), a random access memory(RAM), a register, cache memory, semiconductor memory devices, magneticmedia such as internal hard disks and removable disks, magneto-opticalmedia, and optical media such as CD-ROM disks, and digital versatiledisks (DVDs).

Suitable processors include, by way of example, a general purposeprocessor, a special purpose processor, a conventional processor, adigital signal processor (DSP), a plurality of microprocessors, one ormore microprocessors in association with a DSP core, a controller, amicrocontroller, Application Specific Integrated Circuits (ASICs), FieldProgrammable Gate Arrays (FPGAs) circuits, any other type of integratedcircuit (IC), and/or a state machine.

A processor in association with software may be used to implement aradio frequency transceiver for use in a wireless transmit receive unit(WTRU), user equipment (UE), terminal, base station, radio networkcontroller (RNC), or any host computer. The WTRU may be used inconjunction with modules, implemented in hardware and/or software, suchas a camera, a video camera module, a videophone, a speakerphone, avibration device, a speaker, a microphone, a television transceiver, ahands free headset, a keyboard, a Bluetooth® module, a frequencymodulated (FM) radio unit, a liquid crystal display (LCD) display unit,an organic light-emitting diode (OLED) display unit, a digital musicplayer, a media player, a video game player module, an Internet browser,and/or any wireless local area network (WLAN) module.

What is claimed is:
 1. A wireless transmit/receive unit (WTRU)comprising: circuitry configured to derive a first maximum powerreduction (MPR) and a second MPR; wherein the first and second MPRs arederived at least from a modulation type for an uplink transmission ofthe WTRU; the circuitry further configured to select the first or secondMPR; the circuitry further configured to modify a maximum output powerof the WTRU in response to the selected first or second MPR; and thecircuitry further configured to transmit the uplink transmission at anoutput power not exceeding the modified maximum output power.
 2. TheWTRU of claim 1 wherein the second MPR is derived at least from anadjustment factor.
 3. A method comprising: deriving, by a wirelesstransmit/receive unit (WTRU), a first maximum power reduction (MPR) anda second MPR; wherein the first and second MPRs are derived at leastfrom a modulation type for an uplink transmission of the WTRU;selecting, by the WTRU, the first or second MPR; modifying, by the WTRU,a maximum output power of the WTRU in response to the selected first orsecond MPR; and transmitting the uplink transmission at an output powernot exceeding the modified maximum output power.
 4. The method of claim3 wherein the second MPR is derived at least from an adjustment factor.5. A wireless transmit/receive unit (WTRU) comprising: an antenna; aprocessor, operatively coupled to the antenna, configured to determine afirst maximum power reduction (MPR) and a second MPR; the processorfurther configured to select an MPR based on the determined first andsecond MPR; and the processor further configured to reduce atransmission power level based on the selected MPR.
 6. The WTRU of claim5 wherein the first MPR is compared to the second MPR to select theselected MPR.
 7. A method comprising: determining, by a wirelesstransmit/receive unit (WTRU), a first maximum power reduction (MPR) anda second MPR; selecting, by the WTRU, an MPR based on the determinedfirst and second MPR; and reducing, by the WTRU, a transmission powerlevel based on the selected MPR.
 8. The method of claim 7 wherein thefirst MPR is compared to the second MPR to select the selected MPR. 9.An integrated circuit comprising: circuitry configured to determine afirst maximum power reduction (MPR) and a second MPR; wherein thecircuitry is further configured to selecting an MPR based on thedetermined first and second MPR; and wherein the circuitry is furtherconfigured to control a reduction in transmit power of a wirelesstransmit/receive unit (WTRU) based on the selected MPR.
 10. Theintegrated circuit of claim 9 wherein the first MPR is compared to thesecond MPR to select the selected MPR.